PSO-LFDE Algorithm on Constrained Real-Parameter Optimisation Test Functions

This paper introduces a new version of the particle swarm optimisation (PSO) algorithm particle swarm optimisation with Lévy Flight and the Doppler Effect (PSO-LFDE), maintaining an optimal balance between the exploration and exploitation phases of the optimisation process. The proposed algorithm will hold a better exploration–exploitation equilibrium if the contributions of convergence and diversity and global and individual bests in attracting particles are maintained in balance. The proposed PSO-LFDE algorithm is compared with the PSO algorithm by Gaing on single-objective constrained real-parameter optimisation test functions. The results indicated that the PSO-LFDE has achieved competitive results on the single-objective constrained real-parameter optimisation test functions as compared to PSO algorithm by Gaing. Thus, the PSO-LFDE is validated as a stable, well-designed algorithm and can be a functional alternative approach to deal with various single-objective constrained real-parameter optimisation problems

Application of modified adaptive bats sonar algorithm with doppler effect and levy flight (MABSA-DELF) to optimize mechanical engineering problems

This paper describes the application of the Modified Adaptive Bats Sonar Algorithm with Doppler Effect and Levy Flight (MABSA-DELF) to mechanical engineering design optimization issues. MABSA-DELF is a new algorithm that employs Doppler Effect and Levy Flight theory to improve the position of the transmitted bats’ beam. This project served as a showcase for the superior performance of MABSA-DELF. It was created using the MATLAB software’s computer simulation method. MABSA-DELF demonstrated a superior ability to solve engineering design problems in the fields of business, mechanical/manufacturing engineering, and electrical engineering. MABSA-DELF’s result are compared to those of other established algorithms

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey-Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system

Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Especially on hydraulic positioning system that is highly nonlinear and difficult to be controlled whereby PID parameters needs to be tuned to obtain optimum performance criteria. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey-Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the limit of change in particle velocity and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a good tuning method in the hydraulic positioning system

Optimization of the PID-PD parameters of the overhead crane control system by using PSO algorithm

The development of combination of proportional-integral-derivative and proportional-derivative (PID-PD) controller for overhead crane is presented. Due to the pendulum-like settings, the swinging of load has caused many difficulties while operating the overhead crane. Swinging of the load causes unnecessary tension to the cable and structure of the overhead crane, which will compromise the safety of operator and other workers. Overhead cranes should have the ability to move the load to desired point as fast as possible while minimizing the load swing and maintaining the accuracy. Proportional-integral-derivative (PID) controller is used for overhead crane positioning and proportional-derivative (PD) controller for load oscillation. New time-domain performance criterion function is used in particle swarm optimization (PSO) algorithm for the tuning of the PID-PD controller rather than the general performance criteria using error of the system. This performance criterion function monitors the performance in terms of rise time, overshoot, settling time and steady state error of the overhead crane system. The performance of the optimised PID-PD controller is verified with simulation in MATLAB. The PSO optimized PID-PD controllers with new performance criterion are shown effective in improving the step response of the overhead crane position as well as controlled the load oscillation

Optimization of the PID-PD parameters of the overhead crane control system by using PSO algorithm

The development of combination of proportional-integral-derivative and proportional- derivative (PID-PD) controller for overhead crane is presented. Due to the pendulum-like settings, the swinging of load has caused many difficulties while operating the overhead crane. Swinging of the load causes unnecessary tension to the cable and structure of the overhead crane, which will compromise the safety of operator and other workers. Overhead cranes should have the ability to move the load to desired point as fast as possible while minimizing the load swing and maintaining the accuracy. Proportional-integral-derivative (PID) controller is used for overhead crane positioning and proportional-derivative (PD) controller for load oscillation. New time-domain performance criterion function is used in particle swarm optimization (PSO) algorithm for the tuning of the PID-PD controller rather than the general performance criteria using error of the system. This performance criterion function monitors the performance in terms of rise time, overshoot, settling time and steady state error of the overhead crane system. The performance of the optimised PID-PD controller is verified with simulation in MATLAB. The PSO optimized PID-PD controllers with new performance criterion are shown effective in improving the step response of the overhead crane position as well as controlled the load oscillation

Pso-based PID speed control of traveling wave ultrasonic motor under temperature disturbance

Traveling wave ultrasonic motors (TWUSMs) have a time varying dynamics characteristics. Temperature rise in TWUSMs remains a problem particularly in sustaining optimum speed performance. In this study, a PID controller is used to control the speed of TWUSM under temperature disturbance. Prior to developing the controller, a linear approximation model which relates the speed to the temperature is developed based on the experimental data. Two tuning methods are used to determine PID parameters: conventional Ziegler-Nichols(ZN) and particle swarm optimization (PSO). The comparison of speed control performance between PSO-PID and ZN-PID is presented. Modelling, simulation and experimental work is carried out utilizing Fukoku-Shinsei USR60 as the chosen TWUSM. The results of the analyses and experimental work reveal that PID tuning using PSO-based optimization has the advantage over the conventional Ziegler-Nichols method